Correction of user input

ABSTRACT

In various example embodiments, a system and method for correction of user input are presented. In one embodiment, a method includes receiving a plurality of user strings, selecting one or more string pairs from the plurality of user strings based on a character operator difference between the first string and the second string being below a threshold number, filtering the one or more string pairs to generate a filtered set of strings pairs representing corrections, and correcting user input in a different session by replacing input that matches a first string in a filtered string pair with a second string in the filtered string pair.

RELATED APPLICATIONS

This application claims the priority benefit of U.S. ProvisionalApplication No. 62/087,702, entitled “SYSTEMS AND METHODS FOR CORRECTIONOF USER INPUT,” filed Dec. 4, 2014, which is hereby incorporated hereinby reference in its entirety.

TECHNICAL FIELD

The present application relates generally to systems and methods forcorrection of user input at a networked system using statistical machinetranslation.

BACKGROUND

A user may interact with a networked system in a wide variety ofdifferent ways. In one example, a user of a network-based publicationsystem provides user terms as input to search for items or productsavailable at the network-based publication system. Examples include auser searching for available products at the network-based publicationsystem.

In other examples, a user posts messages, asks questions, submitsqueries, selects items, views items, purchases items, or provides inputin any other way to a networked system. In many examples, users spellterms incorrectly, use slang terms, shortcuts, acronyms, or provideother input that may not match officially recognized terms. Therefore, asystem may have difficulty identifying what a user desires.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and cannot be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating a networked system, according tosome example embodiments.

FIG. 2 is an illustration depicting user input according to one exampleembodiment.

FIG. 3 is another illustration depicting user input according to oneexample embodiment.

FIG. 4 is a block diagram illustration an input correction systemaccording to one example embodiment.

FIG. 5 is a table illustrating a set of filtered string pairs accordingto one example embodiment.

FIG. 6 is a table illustrating results of an input correction systemaccording to one example embodiment.

FIG. 7 is a table illustrating character bigrams according to oneexample embodiment.

FIG. 8 is a flow chart diagram illustrating one method for correctinguser input, according to one example embodiment.

FIG. 9 is another flow chart diagram illustrating another method forcorrecting user input, according to one example embodiment.

FIG. 10 is a flow chart diagram illustrating one method for correctinguser input, according to another example embodiment.

FIG. 11 is a flow chart diagram illustrating another method forcorrecting user input, according to an example embodiment.

FIG. 12 is a flow chart diagram illustrating one method for correctinguser input, according to another example embodiment.

FIG. 13 is a flow chart diagram illustrating one method for correctinguser input, according to another example embodiment.

FIG. 14 is a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 15 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.

The headings provided herein are merely for convenience and do notnecessarily affect the scope or meaning of the terms used.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

In one example embodiment, a system receives a query for an availableitem at a network-based publication system. The system may not returnsufficient results to please the user. In response, the system mayreceive a subsequent query that includes certain modifications to thequery. The system determines that the subsequent query is a modificationof the earlier query based on a character operator difference between aterm in the earlier query and a term in the subsequent query being belowa threshold number.

In another example embodiment, the system data mines user input frommany users and generates string pairs representing potentialcorrections. In this example embodiment, the first string in the stringpair is selected based on the first string being received in fewer thana threshold number of times and the second string in the string pair isselected based on the second string being received in more than athreshold number of times. In one example, the first string is receivedfewer than 50 times and the second string is received more than 10,000times.

In another example embodiment, the system tracks user response to input.In response to the user initiating increased response with the system,the system determines that the subsequent query more effectivelygenerates search results that are desirable to the user. In one example,the user views, selects, or purchases items based on the subsequentquery having initiated less interactions with the system in response toan earlier query. The system then stores this modification, and maysimilarly correct user input in other user sessions.

In another example embodiment, the system receives search terms frommany different users in their respective user sessions. The search termsmay not yield any search results. In response to several of the userssubmitting user terms that do yield results, and that are sufficientlyclose to the user terms that did not yield search results, the systemassociates the user terms that did not yield search results with a userterm that did yield search results and corrects user input in other usersessions by replacing input that matches the first term with the secondterm.

In certain embodiments, the system monitors traffic between the user(e.g., a computing device for the user), and a networked system. Thesystem may operate as an application being executed by a processor of amachine or may be implemented as middleware. As middleware, the systemmay intercept communications from other applications, such as, but notlimited to, a web browser, a messaging application, a customapplication, a forum application, social networking application, orother, or the like. A networked system may include, but is not limitedto, a network-based publication system, a message board, a news site, aweb site, a feedback forum, a search engine, or any other system thatreceives input from users over a network.

A system as described herein generates string pairs included in inputfrom users. In one example, the string pairs include a first string anda second string, where the second string is a correction of the firststring. The second strings in the string pairs may also include othersearch terms that are similar to the original search terms. In oneexample, “similar” means that the first search term and the secondsearch term are within a character operator difference as will bedescribed below. In other examples, the first string is spelledcorrectly, but the second string more accurately matches an item becausemore users use the second term to describe the item. For example, moreusers may use an acronym, slang terms, or other terms to describe anitem that may not be recognized by a manufacturer of the product.

Using a character operator difference, as will be shown, increases thequality of string pairs. String pairs that do not reflect actualcorrections will be filtered out. In one example embodiment, the stringpairs are determined based on a character difference threshold todetermine user input that includes similar terms.

After a number of string pairs are generated, the system may filterstring pairs that are not actual corrections. For example, the systemmay filter out string pairs that include numerical values. For example,a user may change a query from shoes size 7 to shoes size 7.5. In thisexample, although the queries may be associated due to a small characterdifference (e.g., two), the string pair is filtered out because thedifferent characters between the search terms is a numerical value(e.g., from 7 to 7.5).

In another example, the system may filter out string pairs where thesupposed correction is an acceptable search term. For example, wheremany users (e.g., dozens or hundreds) corrected a query from fantasticcleaner to Fantastik cleaner the string pair may be filtered out becausethe potentially corrected term Fantastik is used by a sufficient numberof users. This is the case even though “Fantastik” may not be thecorrect spelling of “fantastic.”

In another example, a user may correct a search query from Nike shoes to“Nike Shoes.” Although a character operator difference between the twoqueries is two (for the two quote characters), the string pair will befiltered out because a regular expression determines that the characterdifference is the addition of quotes. Therefore, these kinds ofcorrections will be filtered out from the set of string pairs resultingin a filtered set of string pairs. According to these and other exampleembodiments, the system filters out string pairs that do not representactual corrections of search terms. The system then appliescharacter-based statistical machine translation that is trained on theset of string pairs. The system then replaces user input in a differentuser session by replacing input that matches a first string in a stringpair with the second string in the string pair.

With reference to FIG. 1, an example embodiment of a high-levelclient-server-based network architecture 100 is shown. A networkedsystem 102, in the example forms of a network-based publication systemor payment system, provides server-side functionality via a network 104(e.g., the Internet or wide area network (WAN)) to one or more clientdevices 110. An input correction system 150 may be implemented as partof the client device 110 (e.g., input correction system 150B), or aspart of the network system 102 (e.g., input correction system 150A). Inanother example embodiment, the input correction system 150 is partiallyimplemented at the input correction system 150A and partiallyimplemented at the input correction system 150B. FIG. 1 illustrates, forexample, a web client 112 (e.g., a browser, such as the InternetExplorer® browser developed by Microsoft® Corporation of Redmond, Wash.State), client application(s) 114, and the input correction system 150Bexecuting on client device 110.

The client device 110 may comprise, but is not limited to, a mobilephone, desktop computer, laptop, portable digital assistants (PDAs),smart phones, tablets, ultra books, netbooks, laptops, multi-processorsystems, microprocessor-based or programmable consumer electronics, gameconsoles, set-top boxes, or any other communication device that a usermay utilize to access the networked system 102. In some embodiments, theclient device 110 comprises a display module (not shown) to displayinformation (e.g., in the form of user interfaces). In furtherembodiments, the client device 110 comprises one or more of a touchscreens, accelerometers, gyroscopes, cameras, microphones, globalpositioning system (GPS) devices, and so forth. The client device 110may be a device of a user that is used to perform a transactioninvolving digital items within the networked system 102. In oneembodiment, the networked system 102 is a network-based publicationsystem that responds to requests for product listings, publishespublications comprising item listings of products available on thenetwork-based publication system, and manages payments for transactions.One or more portions of the network 104 may be an ad hoc network, anintranet, an extranet, a virtual private network (VPN), a local areanetwork (LAN), a wireless LAN (WLAN), a wide area network (WAN), awireless WAN (WWAN), a metropolitan area network (MAN), a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), acellular telephone network, a wireless network, a WiFi network, a WiMaxnetwork, another type of network, or a combination of two or more suchnetworks.

The client device 110 may include one or more applications (alsoreferred to as “apps”) such as, but not limited to, a web browser,messaging application, electronic mail (email) application, ane-commerce site application (also referred to as a marketplaceapplication), and the like. In some embodiments, if the e-commerce siteapplication is included in a given one of the client device 110, thenthis application is configured to locally provide the user interface andat least some of the functionalities with the application configured tocommunicate with the networked system 102, on an as-needed basis, fordata or processing capabilities not locally available (e.g., access to adatabase of items available for sale, to authenticate a user, to verifya method of payment). Conversely if the e-commerce site application isnot included in the client device 110, the client device 110 may use itsweb browser to access the e-commerce site (or a variant thereof) hostedon the networked system 102.

One or more users 106 may be a person, a machine, or other means ofinteracting with the client device 110. In example embodiments, the user106 is not part of the network architecture 100, but may interact withthe network architecture 100 via the client device 110 or other means.For instance, the user 106 provides input (e.g., touch screen input oralphanumeric input) to the client device 110 and the input iscommunicated to the networked system 102 via the network 104. In thisinstance, the networked system 102, in response to receiving the inputfrom the user 106, communicates information to the client device 110 viathe network 104 to be presented to the user 106. In this way, the user106 can interact with the networked system 102 using the client device110.

An application program interface (API) server 120 and a web server 122are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 140. The application server(s) 140may host one or more publication systems 142 and payment systems 144,each of which may comprise one or more modules or applications and eachof which may be embodied as hardware, software, firmware, or anycombination thereof. The application server(s) 140 are, in turn, shownto be coupled to one or more database server(s) 124 that facilitateaccess to one or more information storage repositories or database(s)126. In an example embodiment, the database(s) 126 are storage devicesthat store information to be posted (e.g., publications or listings) tothe publication system(s) 142. The database(s) 126 may also storedigital item information in accordance with example embodiments.

Additionally, a third party application 132, executing on third partyserver(s) 130, is shown as having programmatic access to the networkedsystem 102 via the programmatic interface provided by the API server120. For example, the third party application 132, utilizing informationretrieved from the networked system 102, supports one or more featuresor functions on a website hosted by the third party. The third partywebsite, for example, provides one or more promotional, marketplace, orpayment functions that are supported by the relevant applications of thenetworked system 102.

The publication system(s) 142 provide a number of publication functionsand services to users 106 that access the networked system 102. Thepayment system(s) 144 may likewise provide a number of functions toperform or facilitate payments and transactions. While the publicationsystem(s) 142 and payment system(s) 144 are shown in FIG. 1 to both formpart of the networked system 102, it will be appreciated that, inalternative embodiments, each system 142 and 144 may form part of apayment service that is separate and distinct from the networked system102. In some embodiments, the payment systems 144 may form part of thepublication system 142.

The input correction system 150 provides functionality operable tocorrect user input based on statistical corrections from one or moreusers in a variety of different ways. The input correction system 150may store input received from the users in the database(s) 126, thirdparty server(s) 130, and other sources. In one example embodiment, theinput correction system 150 operates as part of the application server140 and intercepts user input to the networked system 102. In anotherembodiment, the input correction system 150 operates as part of theclient device 110 and intercepts input provided by the user 106.

Further, while the client-server-based network architecture 100 shown inFIG. 1 employs a client-server architecture, the present inventivesubject matter is of course not limited to such an architecture, andcould equally well find application in a distributed, or peer-to-peer,architecture system, for example. The various publication system(s) 142,payment system(s) 144, and input correction system 150 could also beimplemented as standalone software programs, which do not necessarilyhave networking capabilities.

The web client 112 may access the various publication and paymentsystems 142 and 144 via the web interface supported by the web server122. The input correction system 150A may gather the various user inputoriginating at the client device 110. Similarly, the input correctionsystem 150A may accesses the various services and functions provided bythe publication and payment systems 142 and 144 via the programmaticinterface provided by the API server 120. The input correction system150 may also operate, at least partially, as part of a sellerapplication (e.g., the Turbo Lister application developed by eBay® Inc.,of San Jose, Calif.) to enable sellers to author and manage listings onthe networked system 102 in an off-line manner, and to performbatch-mode communications with the networked system 102.

FIG. 2 is an illustration depicting user input according to one exampleembodiment 200. In this example, the input correction system 150receives a first query 202 from a user that includes the user terms“NIKE AIR HURACHE.” The user may not initiate any user actions inresponse to this query because of the misspelled term “Hurache” causingno search results to be returned. Later, in the same user session, theuser submits a second query 204 that includes the user terms “REEBOKSHOES.” The user then also views one of the search results but causes noother user events. Later, in the same user session, the user submits asearch query 206 that includes the user terms “NIKE AIR HARACHE 7,” andagain views one of the search results. After the previous query 206, theuser submits a search query 208 that includes the user terms “NIKE AIRHUARACHE.” The user then initiates several user events based on thesearch results. For example, the user selects a search result, inquiresabout the search results, and purchases an item that was included in thesearch results.

In another example embodiment, the input correction system 150 receivesthe strings in many different user sessions. In one example embodiment,the input correction system 150 selects string pairs from the userinput, wherein strings in the string pair differ by a threshold numberof character operators. For example, in response to the threshold numberof character operators being three, the input correction system 150selects two string pairs, such as, but not limited to, (NIKE AIRHURACHE, NIKE AIR HURACHE 7) and (NIKE AIR HURACHE, NIKE AIR HUARACHE).The first string pair is selected because the character difference istwo characters “7,” and the second string pair is selected because thecharacter operator difference is one (the additional ‘A’ in “HUARACHE”).Furthermore, the input correction system 150 determines that the secondstring in each string pair resulted in increased response from the useras compared with the first string in the string pair.

In one example embodiment, the input correction system 150 filters thestring pairs to generate a filtered set of string pairs. The filteredset of string pairs includes string pairs that have been determined tobe corrections. For example, the second string in the string pair is auser correction of the first string in the string pair. In this example,the input correction system 150 includes the second string pair (NIKEAIR HURACHE, NIKE AIR HUARACHE) in a filtered set of string pairs. Inthis example, the input correction system 150 determined that the firststring pair (NIKE AIR HURACHE, NIKE AIR HURACHE 7) was not a correctionbecause of the mere addition of a numerical value.

In one example embodiment and in a different user session, the inputcorrection system 150 replaces “NIKE AIR HURACHE” that is included inuser input with “NIKE AIR HUARACHE” based on the string pair in thefiltered set of string pairs.

In other example embodiments, the input correction system 150 receivesthe input without regard to user sessions. In this example embodiment,the input correction system 150 receives the queries and assembles thequeries in the database 126. The input correction system 150 thengenerates the string pairs as previously described.

In another example, the string pairs also include a category. Forexample, a string pair that includes “NIKE HUARACHE” may be included ina “shoes” category, and/or an “apparel” category.

In one example embodiment, the input correction system 150 receives userinput and matches user input with one or more term in generated stringpairs. For example, the user input may include the term “chaito.”“Chaito” may be included in more than one string pair. In one example, astring pair includes (Chaito, Casio) and may be in an electronicscategory. In another example, a string pair includes (Chaito, Chair) andmay be in a furniture category. In response to receiving user input froma publisher that sells furniture, the input correction system 150selects the (Chaito, Chair) string pair. In response to receiving theuser input from a publisher that sells electronics, the input correctionsystem 150 selects the (Chaito, Casio) string pair. Therefore, the inputcorrection system 150 may consider a category of the string pair andcorrect user input using a string pair that matches the category of apublisher of the user input.

In one example embodiment, the input correction system 150 generates acategory vector for each category associated with a user term. The inputcorrection system 150 may also classify the user input into one of thecategory vectors using a classification system as one skilled in the artmay appreciate. Weights returned from the classification system may formthe category vector for the user input. The input correction system 150then computes a differential between the category vector for the userinput and the category vector for each category. The input correctionsystem 150 may determine the category to use for the user input based,at least in part, on the category vector with the least differentialbetween the category vector and the user input category vector. In oneexample, the differential is the product of the category vector and theuser input category vector.

In another example embodiment, the input correction system 150 trains ona set of user input and is configured to generate a probability for oneor more categories. In response to user input matching the first term ina string pair, the input correction system 150 may determine matchingprobabilities for the categories and selects the string pair that isincluded in the category with the highest probability.

FIG. 3 is another illustration depicting user input according to oneexample embodiment. In this example, the input correction system 150receives a first query 302A from a first client device 110A. A user ofthe client device 110A may not initiate any user events in response toreceiving search results based on the first query 302A. Later, in thesame session, the client device 110A provides other input 304 a that maynot be related to the first query 302A. Also, later in the same session,the client device 110A provides a second query 306A that includes thesearch terms “NIKE AIR HUARACHE.” The user of the client device 110Athen initiates a selection event.

In other user sessions, such as sessions between client devices 110B,110C and the networked system 102, respective users of the clientdevices 110B, 110C submit queries 302B and 302C. As with client device110A, the misspelled user input may not yield sufficient search resultsto motivate the user to select or view one of the search results.

In the same respective sessions, the users may provide other input 304B,304C and may subsequently submit search queries 306B, 306C that includethe correctly spelled “NIKE AIR HUARACHE.” In response, the respectiveusers of client devices 110B, 110C initiate user events. For example,the user of client device 110B submits an inquiry regarding one of theitems returned in search results based on the query 306B. In anotherexample, the user of the client device 110C purchases one of the itemsreturned in search results based on the query 306C.

In one example embodiment, the input correction system 150 selectsstring pairs from the user input in the user sessions for the clientdevices 110A, 110B, 110C, wherein the strings in the string pair differby a threshold number of character operators. For example, in responseto the threshold number of character operators being three, the inputcorrection system 150 selects the string pair (NIKE AIR HURACHE, NIKEAIR HUARACHE). Furthermore, the input correction system 150 determinesthat the respective queries 306 based on the second string in the stringpair resulted in user events, whereas the first string in the stringpair did not. In one example embodiment and in a different user session,the input correction system 150 replaces “NIKE AIR HURACHE” that isincluded in user input with “NIKE AIR HUARACHE” based on the string pairin the filtered set of string pairs.

In another example embodiment, the string pair includes substrings ofuser input. In one example, the string pair previously describedincludes (Hurache, Huarache). Therefore, the input correction system 150is not limited regarding the user input and may determine strings asindependent user terms divided by a space or punctuation mark, or othercharacter, or the like.

FIG. 4 is a block diagram illustrating an input correction system 450(which may be the input correction system 150) for correcting user inputaccording to one example embodiment 400. In one example embodiment, theinput correction system 450 includes an input module 420, a selectionmodule 440, a filter module 460, and a correction module 480.

The input module 420, in certain examples, is configured to receive userinput to the networked system 102. The input module 420 parses thevarious user inputs with the networked publication system 142 andgenerates strings representing the user input. For example, the stringsmay include queries, selections, views, or other actions by the user ina user session.

In certain embodiments, the user may alter previous input and provide asubsequent correction of the previous input. The correction may includecorrecting misspelled words, but of course, this is not necessarily thecase. In other examples, the subsequent query may include other termsrecognizable by a user, although the other terms may or may not bespelled correctly according to a spelling authority, such as, but notlimited to, a dictionary, a language model, or the like.

In one embodiment, the selection module 440 generates string pairs byassociating an earlier string with a later string (in the same session)where a character operator difference between the two strings is below athreshold value. Of course, this not necessarily the case, as theselection module 440 may select string pairs in a large database of userinput terms. As described herein, a character operator differenceincludes a number of character operations needed to alter the earlierstring into the later string. For example, adding a character is onecharacter operation, removing a character is another operation,substituting one character for another is another character operation,and transposing two characters is a single character operation.Therefore, a character difference between two strings may be describedas a character operator difference.

In a specific example, the character operator difference may be based ona Damerau-Levenshtein distance as one skilled in the art may appreciate.In one specific example, the character operator difference may be threeand the selection module 440 associates strings with a characteroperator distance of three or less. Of course, other values may be used,and this disclosure is not limited in this regard.

In one embodiment, the selection module 440 limits association to userinput that was received in the same session. In one example, the inputmodule 420 may limit a user session to a threshold number of seconds.For example, the threshold number of seconds may be 20 seconds.Therefore, according to some example embodiments, the selection module440 may not associate strings that were received more than 20 secondsapart.

The filter module 460 is configured to filter the generated pairs ofstrings, where the second of the strings in the pair is not a correctionto the first of the strings in the pair, resulting in a filtered set ofstring pairs. As previously described, the selection module 440generates pairs of strings; however, many of the string pairs are notactual corrections.

In one example, a string pair includes (iphone 5, iphone 5s). Althoughthere is a one-character operator difference between the two strings,the second string “iphone 5s” is not a correction to the first string“iphone 5,” but is a refinement. The filter module 460 may determinethat the second string is a refinement because the language model mayrecognize 5s as a correct term. Furthermore, the language model mayrecognize that 5s in the same query as an iphone is particularlyacceptable because 5s may be a recognized model number for an iPhone.The filter module 460 may remove such refinements from the sets ofstring pairs so that remaining pairs represent a high quality set ofspelling corrections (e.g., include actual spelling corrections).

In another example embodiment, the filter module 460 removes a stringpair from the set based on a use ratio being above a threshold value.According to a language model, a term in the strings may be assigned ause value, probability, or frequency. Strings that are commonly used inthe language model result in high values, whereas strings that are notused, or used infrequently, result in lower values. In one example,where a string includes many terms, the use value for the string is alowest use value for the terms in the string. The filter module 460 maydetermine a ratio of these use values to determine a language model useratio.

In one example, where a search term in the first string of a string pairincludes a high frequency of use (e.g., the term is commonly used in thelanguage) and the potentially corrected term in the second string of thestring pair includes a low frequency of use (e.g., the potentiallycorrected term is either not recognized or is not generally used in thelanguage), a language model use ratio of the first term to the secondterm may be higher than one. Such a ratio value may further suggest thatthe string pair is not an actual correction because the first term ismore widely used than the second term according to the language model.

Furthermore, where a search term in the second string includes a highuse frequency, according to the language model, the language model ratiowill be a much lower value. Therefore, string pairs where the secondstring is a correction of the first string will likely have a languagemodel ratio that is lower than one. Accordingly, in one exampleembodiment, the filter module 460 removes string pairs with a languagemodel ratio that is lower than one. Of course, other values may be used,and this disclosure is not limited in this regard. Therefore, in oneexample embodiment, the filtered set of string pairs includes stringpairs having a language model use ratio above a threshold value.

In one example, the string pair may be (Bluetooth earphones, Bluetoothearphones). Because the terms in the first string are correctly spelled,they will have a high use frequency according to the language model.Because the terms in the second string are not correctly spelled, thesecond string will have a low use frequency according to the languagemodel. Accordingly, a use ratio of the first string to the second stringwill be higher than one, and the filter module 460 will remove this pairfrom the set. This is beneficial because the string pair does notindicate a corrected spelling.

In another example embodiment, the string pair includes a pluralitychange. For example, the string pair may be (HD DVD, HD DVDs). Althougha character operator difference between the two strings is one, thesecond string is a plurality change of the first string. Therefore, thestring pair does not indicate a spelling correction, and the filtermodule 460 may remove the string pair from the set. In this exampleembodiment, the filter module 460 does not include the string pair inthe filtered set of string pairs because the string pair is not acorrection.

In another example embodiment, the filter module 460 removes a stringpair from the set based on results from a regular expression. In oneexample, a string pair may be (Nike shoes, “Nike Shoes”) (the secondstring including the quote characters in the string). As one skilled inthe art may appreciate, a regular expression may detect the quotecharacter beginning and terminating the second string. Accordingly, inresponse to a character operator difference between the two stringsbeing two (for the two quote characters) and the regular expressionindicating the presence of the quote characters, the filter module 460may remove the string pair. This is beneficial because the addition ofquotes does not likely indicate a spelling correction. Of course, oneskilled in the art may produce many different regular expressions todetect similar modifications, and this disclosure is not limited in thisregard. In this example embodiment, the filter module 460 does notinclude this string pair in the filtered set of string pairs because thestring pair is not a correction.

In another example embodiment, the filter module 460 removes a stringpair from the set based on the second string in the pair being arefinement of the first string in the pair. In one example, where adifference between the first string and the second string includes amissing term or an additional term, the pair does not likely indicate aspelling correction. In one example, a string pair may be (t-shirt,t-shirt xl). Although a character operator difference between thestrings is three, the addition of a term does not likely indicate aspelling correction, and the filter module 460 may remove the stringpair from the set resulting in a filtered set of string pairs.Furthermore, because the added term is a recognized acronym (e.g.,extra-large), the string pair does not represent a spelling correction.

In another example, a string pair includes (shoes size 7, shoes size7.5). The filter module 460 may not include the string pair in afiltered set of string pairs because the character difference betweenthe two strings consists of a numerical value. Numerical changes morelikely reflect a refinement instead of a spelling correction. Therefore,filtering these kinds of string pairs results in a set of string pairsthat more likely represents actual spelling corrections.

In another example embodiment, the filter module 460 removes a stringpair from the set in response to search terms in one of the stringsbeing a predetermined language. For example, a user may configure thefilter module 460 to remove string pairs that include one or moreSpanish terms. In response, the filter module 460 removes string pairsthat include Spanish terms.

Therefore, in one example embodiment, a filtered set of string pairsincludes string pairs in a certain language and may not include stringsin other languages. Although some of the string pairs may include termsfrom other languages, the number likely represents a small percentage ofstring pairs, but the set also more likely includes more spellingcorrections. This is because, in some examples, a misspelled Englishterm may closely represent a term from another language, and it is lessdesirous to remove this kind of spelling correction from the set.

The correction module 480, in certain examples, is configured to train astatistical machine translation framework according to pairs of stringsremaining in the set. The correction module 480 receives input fromanother user and may correct spelling in the input based on misspelledsearch terms matching search terms in one or more of the string pairs.

The spelling correction framework includes splitting the search termsinto sequences of lowercased characters and using a special character tomark term boundaries. After correcting the character sequences accordingto a language model, the correction module 480 merges the correctionsback to full terms.

For training the spelling correction framework, the correction module480 uses one or more software application tools as one skilled in theart may appreciate. Character alignment may be performed using GIZA++¹for 4, 3 and 2 iterations of IBM Model 1, HMM, and IBM Model 3, as oneskilled in the art may appreciate. Of course other numbers of iterationsmay be used and this disclosure is not limited in this regard. ¹ FranzJosef Och and Hermann Ney. 2003. A systematic comparison of variousstatistical alignment models. Computational Linguistics, 29(1):19-51.

For phrase extraction, the correction module 480 may use Moses² forstandard phrase extraction, building KenLM³ language models, and tuning.The correction module 480 may further optimize weights for the modelsusing MERT.⁴

In another example embodiment, the correction module 480 uses bigramcharacters instead of single characters, as suggested in Tiedemann,⁵ inorder to improve the statistical alignment models and make them moreexpressive. ² Philipp Koehn, Hieu Hoang, Alexandra Birch, ChrisCallison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, WadeShen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, AlexandraConstantin, and Evan Herbst. 2007. Moses: Open source toolkit forstatistical machine translation. In Proceedings of the 45th AnnualMeeting of the Association for Computational Linguistics: Demo andPoster Sessions, pages 177-180, Prague, Czech Republic, June.³ KennethHeafield. 2011. KenLM: faster and smaller language model queries. InProceedings of the EMNLP 2011 Sixth Workshop on Statistical MachineTranslation, pages 187-197, Edinburgh, UK, July.⁴ Franz Josef Och. 2003.Minimum error rate training in statistical machine translation. InProceedings of the 41st Annual Meeting of the Association forComputational Linguistics, pages 160-167, Sapporo, Japan, July.

In another example embodiment, the selection module 440 uses a languagemodel trained on publicly available corpora, frequent queries, or webcontent from the networked marketplace to calculate log-likelihoods foreach string. If a use ratio (e.g., 6) of the log-likelihood first stringin a string pair to the second string in the string pair is below agiven threshold value (e.g., 1.01), the selection module 440 may notremove the string pair from consideration by the correction module 480.In one example where the string pair is represented as (x,y) and p(x) isthe likelihood of x, and p(y) is the likelihood of y, the language modeluse ratio may be determined using Equation 1. ⁵ Jörg Tiedemann. 2012.Character-based pivot translation for under-resourced languages anddomains. In Proceedings of the 13th Conference of the European Chapterof the Association for Computational Linguistics, pages 141-151,Avignon, France, April.

$\begin{matrix}{\delta > {\frac{\log \; {p(x)}}{\log \; {p(y)}}.}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In another example embodiment, the selection module 440 removes stringpairs in response to the second string in the string pair including aterm insertion, term deletion, or numerical values. In one example, astring pair of (polo shirt, polo shirt xl) is removed because the secondstring in the pair includes an extra term. In another example, thestring pair (nikon d700, nikon d7100) is removed because the characterdifferences include a numerical value or the terms d700 and d7100 arerecognized models for a Nikon™ camera. Therefore, in certainembodiments, a filtered set of string pairs does not include stringpairs where the second string simply includes an additional term, orwhere the character difference between the first string in the stringpair and the second string in the string pair is a numerical value.

In another example embodiment, the selection module 440 removes a stringpair in response to differences in the second string in the pairincluding known words or terms. For example, a string pair (snake batwooden, snake bat wood) is removed because the term “wood” is well knownaccording to a language model. In another example, the string pair (hddvds, hd dvd) is removed because the changed term (“dvd”) is arecognized term.

In another example embodiment, the selection module 440 removes a stringpair in response to the string pair definitely being a language that isinconsistent with the current language model. For example, where Englishis the current language model, a string pair that is clearly Spanish orclearly an unknown language is removed from the set of string pairs sothat English string pairs are included in a filtered set of stringpairs, but string pairs that include other languages are not.

FIG. 5 is a table 500 illustrating a set of filtered string pairsaccording to one example embodiment. As previously described, the inputmodule 420 generates string pairs based, at least in part, on characteroperation distances. Several examples of character operation differencesare depicted in FIG. 5. Furthermore, the filter module 460 determineswhether each of the string pairs is a user correction or not and includethe string pairs that are corrections in a filtered set of string pairs.

As depicted in FIG. 5, the string pair (nike air hurache, nike airhuarache) is a string pair with a character operator distance of 1. Thefilter module 460 may not remove this string pair from a set of stringpairs because the second string in the pair represents an actual brandof Nike™ shoes, while the terms in the first string in the pair do not.The string pair (Jordan size 9, Jordan size 9.5) is filtered out by thefilter module 460 because the character difference includes a numericalvalue as previously described.

The filter module 460 may remove the string pair (galaxy s4, galaxy s5)because a “galaxy s5” is a recognized product. Therefore, the stringpair does not represent an actual spelling correction. The filter module460 may not remove the string pair (power cord for playstation 3, powercord for playstation 3) because the string pair represents an actualspelling correction. Therefore, in certain example embodiments, aspelling correction may include two or more terms and this disclosure isnot limited regarding the number of terms correctable by the trainedmachine translation framework. Other examples are illustrated in FIG. 5.

In one example embodiment, the filter module 460 filters string pairsusing a regular expression. For example, the filter module 460 uses aregular expression to determine whether the second string in a stringpair simply includes quote character additions. In response to thefilter module 460 determining that the string pair includes the additionof quotes, the filter module 460 removes the string pair fromconsideration by the correction module 480. In this example embodiment,the filter module 460 does not include string pairs that match theregular expression in the filtered set of string pairs.

FIG. 6 is a table 600 illustrating results of an input correction systemaccording to one example embodiment. As depicted in FIG. 6, applyingthese manual heuristics to generate a filtered set of string pairs, andperforming machine translation using the filtered set of string pairs,demonstrates significant improvement in machine translation spellingcorrections as compared with machine translation using all of the stringpairs and/or machine translation using an entropy based classifier (ME).

FIG. 7 is a table 700 illustrating character bigrams according to oneexample embodiment. In another example embodiment, the correction module480 corrects subsequently received strings into a sequence of lowercasedcharacters and use a special character to denote white space or termboundaries (e.g., an ‘S’). FIG. 7 shows one example of a search querybeing processed using Bigram character analysis as one skilled in theart may appreciate. The received string includes “hollowin costome.” Thecorrection module 480 parses the received string into a character bigramby combining each character with the subsequent character in the string,resulting in “ho of ll lo ow wi in nS Sc cu st to om me.” The correctionmodule 480 then references the language model to correct one or morebigrams as one skilled in the art may appreciate. This results in atranslated bigram that includes “ha al ll lo ow we ee en nS Sc co st toum me.” The correction module 480 then reconstructs the corrected termsusing the translated bigram as indicated in FIG. 7.

FIG. 8 is a flow chart diagram illustrating one method 800 forcorrecting user input, according to one example embodiment. Operationsin the method 800 may be performed by one or more of the modulesdescribed in FIG. 4. The method 800 includes operations 810, 820, 830,and 840.

In one example embodiment, the method 800 begins at operation 810, andthe input module 420 receives a plurality of user strings in a usersession. The string pairs, in one example embodiment, are based oncharacter operator differences between the strings in the respectivepairs.

The method 800 continues at operation 820, and the selection module 440selects one or more string pairs from the plurality of user strings. Thestring pairs include a first string and a second string, and theselection module 440 may select the string pairs based on a characteroperator difference between the first string and the second string beingbelow a threshold number.

In one example embodiment, the second string results in increasedresponse from the user as compared with the first string. For example,the user may initiate more events following submission of the secondstring in the string pair than with the first string in the string pair.

The method 800 continues at operation 830, and the filter module 460filters the string pairs one or more string pairs to generate a filteredset of strings pairs. The filtered set of string pairs includes stringpairs where the second string in each string pair is a correction of therespective first string in the string pair.

In one embodiment, the correction corrects a misspelled term to acorrectly spelled term according to a language dictionary. However, thisis not necessarily the case. In other embodiments, the correctionchanges a correctly spelled term according to a language model to a termthat is more recognizable by the user, although the correction mayresult in a term that is not spelled correctly according to a languagemodel. Therefore, a correction comprises an alteration of a term fromone form to another that may be more readily recognized by a user evenif the result is a word that is not correctly spelled according to anofficial spelling source.

The method 800 continues at operation 840, and the correction module 480corrects user input in a different session by replacing input thatmatches a first string in a filtered string pair with a second string inthe filtered string pair. In another example embodiment, the correctionmodule 480 trains a statistical machine language framework based on thefiltered set of string pairs and corrects user input using thestatistical machine language framework as one skilled in the art mayappreciate.

The input correction system 150, therefore, allows a user to submitmisspelled search terms, and the input correction system 150 accuratelydetermines spelling corrections to determine what the user desires. Thespelling corrections may include changing spelling of a term to be moreconsistent with current trends, fads, popularity, or other factors interm spelling. In one example embodiment, the input correction system150 may recommend a spelling correction to a user, and the user mayaccept or rejection the correction.

In one example, where many people misspell a term, a statisticalanalysis system determines that the spelling is correct because,statistically, people tend to spell the term in that specific way.Therefore, combining statistical analysis to determine spellingcorrections and using a language model to filter the corrections resultsin increased accuracy for spelling corrections using statistical machinetranslation.

A system as described herein considers full string pairs as trainingdata for training a statistical engine. Furthermore, the inputcorrection system 150, in one example embodiment, uses standardphrase-based machine translation modeling to derive phrase and lexicaltranslation models for previous and subsequent user strings.

FIG. 9 is another flow chart diagram illustrating another method 900 forcorrecting user input, according to one example embodiment. Operationsin the method 900 may be performed by one or more of the modulesdescribed in FIG. 4. The method 900 includes operations 910, 920, 930,and 940.

The method 900 begins, and at operation 910, the input module 420receives a plurality of user strings in a user session. The method 900continues, and at operation 920, the selection module 440 selects twostrings from the user session that are within a threshold characteroperator difference and generates a string pair that includes the twostrings. In another example embodiment, the input module 420 receivesthe user strings in many different user sessions.

The method 900 continues, and at operation 930, the filter module 460determines whether either string in the string pair includes a term thatis part of a predetermined language. In one example, a user of the inputcorrection system 150 indicates the predetermined language. In anotherexample, the user indicates a preferred language and the inputcorrection system 150 interprets other languages to be predeterminedlanguages. For example, the user indicates that Spanish as thepredetermined language, and the filter module 460 removes string pairsthat include Spanish terms. In another example, the user indicates thatEnglish is the preferred language, and the filter module 460 removesstring pairs that include terms in any other language. The filter module460, accordingly, generates a filtered set of string pairs that includesterms in a specified language.

In response to the filter module 460 determining that the string pairincludes a term from a predetermined language, the method 900 continuesat operation 920. In response to the filter module 460 determining thatthe string pair does not include any term from the predeterminedlanguage, the method 900 continues at operation 940, and the correctionmodule 480 corrects user input in another user session based on thestring pairs that are included in the filtered set.

FIG. 10 is a flow chart diagram illustrating one method 1000 forcorrecting user input, according to another example embodiment.Operations in the method 1000 are performed by one or more of themodules described in FIG. 4. The method 1000 includes operations 1010,1020, 1030, and 1040.

The method 1000 begins, and at operation 1010, the input module 420receives a plurality of user strings in a user session. The method 1000continues at operation 1020, and the selection module 440 selects twostrings from the user session that are within a threshold characteroperator difference. The method 1000 continues at operation 1030, andthe selection module 440 determines whether the second of the twostrings yields increased response from the user as compared with thefirst of the two strings. In response to the second of the two stringsnot yielding increased response from the user, the method 1000 continuesat operation 1020 and the selection module 440 selects another string aspreviously described.

In response to the second of the two strings yielding increased responsefrom the user, the method 1000 continues at operation 1040, and thecorrection module 480 corrects user input in a different user sessionbased on the string pair. In another example embodiment, the correctionmodule 480 trains a statistical machine translation framework using afiltered set of string pairs that includes the string pair described.The correction module 480 then corrects input in a different usersession based on the statistical machine translation framework.

In another example embodiment of the method 1000, the filter module 460stores the string pair in a set of filtered string pairs. The correctionmodule 480 then further trains the statistical machine translationframework based on the set of filtered string pairs.

FIG. 11 is a flow chart diagram illustrating another method 1100 forcorrecting user input, according to an example embodiment. Operations inthe method 1100 are performed by one or more of the modules described inFIG. 4. The method 1100 includes operations 1110, 1120, 1130, 1140, and1150.

The method 1100 begins, and at operation 1110, the input module 420receives a plurality of user strings in a user session. The method 1100continues at operation 1120, and the selection module 440 selects twostrings from the user session that are within a threshold characteroperator difference.

The method 1100 continues at operation 1130, and the filter module 460determines whether a language use model for the first string in a stringpair is above a threshold value. In one example embodiment, a languageuse model indicates a frequency of use for a string in a given language.Therefore, according to the language use model, the filter module 460determines whether the first string in a string pair is sufficientlyrecognized. For example, the language use model may indicate that thefirst string has a language use model above a threshold value and inresponse, the method 1100 continues at operation 1120. In this way, thefilter module 460 does not include the string pair in a filtered set ofstring pairs. This ensures that the filtered set of string pairsincludes string pairs that represent actual corrections by removingstring pairs where the first string in the string pair is a recognizedterm in a given language.

In response to the language use model for the first string in the stringpair being below the threshold value, the method 1100 continues atoperation 1140. At operation 1140, the filter module 460 determineswhether a language use model ratio is above a threshold value. In oneexample, the filter module 460 retrieves a language use model value foreach string in the string pair and determines the use model ratio bydividing the use model value for the first string in the string pair bythe use model value for the second string in the string pair. A languageuse model ratio that is above 1.0 may indicate that first string in thestring pair is more frequently used in a given language than the secondstring in the string pair. Therefore, the string pair likely does notrepresent a correction. A language use model ratio that is 0.5 or lowerindicates that the second string in the string pair is more prevalent inthe given language than the first string in the string pair. Therefore,the language use model ratio is another indicator of whether the stringpair represents a correction or not.

In response to the language use model ratio being above a thresholdvalue at operation 1140, the method 1100 continues at operation 1120. Inresponse to the language use model ratio not being above the thresholdvalue, the method 1100 continues at operation 1150, and the correctionmodule 480 corrects user input based on the string pair as previouslydescribed. In another example embodiment, the correction module 480trains a statistical machine translation framework using a filtered setof string pairs that includes the string pair described.

FIG. 12 is a flow chart diagram illustrating one method 1200 forcorrecting user input, according to another example embodiment.Operations in the method 1200 are performed by one or more of themodules described in FIG. 4. The method 1200 includes operations 1210,1220, 1230, and 1240.

The method 1200 begins, and at operation 1210, the input module 420receives one or more user terms in a user session. The user terms may bereceived textually, numerically, via a selection, via a control at agraphical user interface, or by any other method as one skilled in theart may appreciate.

The method 1200 continues at operation 1220, and the selection module440 selects two terms from the user session that are within a thresholdcharacter operator difference. The method 1200 continues at operation1230, and the filter module 460 determines whether the second term is acorrection of the first term in the two strings. In response to thefilter module 460 determining that the second term is not a correctionof the first term, the method 1200 continues at operation 1220. Inresponse to the filter module 460 determining that the second term is acorrection of the first term, the method 1200 continues at operation1240. At operation 1240, the correction module 480 corrects user inputin a different user session based on the two terms. In another exampleembodiment, the correction module 480 trains a language statisticalmachine framework using a set of filtered string pairs that includes thetwo terms and corrects the user input based on output of the statisticalmachine framework.

FIG. 13 is a flow chart diagram illustrating one method 1300 forcorrecting user input, according to another example embodiment.Operations in the method 1300 are performed by one or more of themodules described in FIG. 4. The method 1300 includes operations 1310,1320, 1340, 1350, and 1360.

The method 1300 begins, and at operation 1310, the input module 420receives a plurality of user strings in a user session. The method 1300continues at operation 1320, and the selection module 440 selects one ormore string pairs from the user session that are within a thresholdcharacter operator difference. The method 1300 continues at operation1340, and the filter module 460 filters the string pairs by removingstring pairs that do not represent corrections resulting in a filteredset of string pairs.

The method 1300 continues at operation 1350, and the input module 420receives an input query from a user in a different user session, wherethe input query includes a first string in a filtered string pair. Themethod 1300 continues at operation 1360, and the correction module 480returns search results based on use of the second string in the filteredstring pair.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Machine and Software Architecture

The modules, methods, applications and so forth described in conjunctionwith FIGS. 2-13 are implemented, in some embodiments, in the context ofa machine and an associated software architecture. The sections belowdescribe representative software architectures and machine (e.g.,hardware) architecture that are suitable for use with the disclosedembodiments.

Software architectures are used in conjunction with hardwarearchitectures to create devices and machines tailored to particularpurposes. For example, a particular hardware architecture coupled with aparticular software architecture will create a mobile device, such as amobile phone, tablet device, or so forth. A slightly different hardwareand software architecture may yield a smart device for use in the“internet of things” while yet another combination produces a servercomputer for use within a cloud computing architecture. Not allcombinations of such software and hardware architectures are presentedhere as those of skill in the art can readily understand how toimplement the inventive subject matter in different contexts from thedisclosure contained herein.

Software Architecture

FIG. 14 is a block diagram 1400 illustrating a representative softwarearchitecture 1402, which may be used in conjunction with varioushardware architectures herein described. FIG. 14 is merely anon-limiting example of a software architecture 1402, and it will beappreciated that many other architectures may be implemented tofacilitate the functionality described herein. The software architecture1402 may be executing on hardware such as machine 1500 of FIG. 15 thatincludes, among other things, processors 1510, memory/storage 1530, andI/O components 1550. A representative hardware layer 1404 is illustratedand can represent, for example, the machine 1500 of FIG. 15. Therepresentative hardware layer 1404 comprises one or more processingunits 1406 having associated executable instructions 1408. Executableinstructions 1408 represent the executable instructions of the softwarearchitecture 1402, including implementation of the methods, modules andso forth of FIGS. 2-13. Hardware layer 1404 also includes memory and/orstorage 1410, which also have executable instructions 1408. Hardwarelayer 1404 may also comprise other hardware 1412 which represents anyother hardware of the hardware layer 1404, such as the other hardwareillustrated as part of machine 1500.

In the example architecture of FIG. 14, the software architecture 1402may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1402includes layers, such as, an operating system 1414, libraries 1416,frameworks/middleware 1418, applications 1420 and presentation layer1444. Operationally, the applications 1420 or other components withinthe layers may invoke application programming interface (API) calls 1424through the software stack and receive a response, returned values, andso forth illustrated as messages 1426 in response to the API calls 1424.The layers illustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile or specialpurpose operating systems may not provide a frameworks/middleware 1418layer, while others may provide such a layer. Other softwarearchitectures may include additional or different layers.

The operating system 1414 manages hardware resources and provide commonservices. The operating system 1414 may include, for example, a kernel1428, services 1430, and drivers 1432. The kernel 1428 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1428 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1430 may provideother common services for the other software layers. The drivers 1432are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1432 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 1416 provide a common infrastructure that may be utilizedby the applications 1420 or other components and/or layers. Thelibraries 1416 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than to interfacedirectly with the underlying operating system 1414 functionality (e.g.,kernel 1428, services 1430 and/or drivers 1432). The libraries 1416 mayinclude system libraries 1434 (e.g., C standard library) that providefunctions such as memory allocation functions, string manipulationfunctions, mathematic functions, and the like. In addition, thelibraries 1416 may include API libraries 1436 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D in a graphic content on a display), database libraries (e.g., SQLitethat may provide various relational database functions), web libraries(e.g., WebKit that may provide web browsing functionality), and thelike. The libraries 1416 may also include a wide variety of otherlibraries 1438 to provide many other APIs to the applications 1420 andother software components/modules.

In one example embodiment, the input correction system 150 isimplemented as an application. In another example embodiment, the inputcorrection system 150 is implemented as a framework and/or middleware.In one example, the input module 420 uses one or more libraries 1416 toreceive or parse user input in a user session. The selection module 440may use one or more libraries 1416 to parse user input, perform bigramanalysis on input strings, apply a regular expression, or perform otherfunctions as described herein. In another example, the filter module 460uses one or more libraries 1416 to store string pairs at a storagedevice (e.g., memory/storage 1410). Also, the correction module 480 mayuse one or more libraries 1416 to replace strings or portions of stringsor perform other operations as described herein.

The frameworks/middleware 1418 (also sometimes referred to asmiddleware) may provide a higher-level common infrastructure that may beutilized by the applications 1420 and/or other softwarecomponents/modules. For example, the frameworks/middleware 1418 mayprovide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1418 may provide a broad spectrum of other APIsthat may be utilized by the applications 1420 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 1420 include built-in applications 1440 or third partyapplications 1442. Examples of representative built-in applications 1440may include, but are not limited to, a contacts application, a browserapplication, a book reader application, a location application, a mediaapplication, a messaging application, or a game application. Third partyapplications 1442 may include any of the built-in applications 1440 aswell as a broad assortment of other applications. In a specific example,the third party application 1442 (e.g., an application developed usingthe Android™ or iOS™ software development kit (SDK) by an entity otherthan the vendor of the particular platform) is mobile software runningon a mobile operating system such as iOS™, Android™, Windows® Phone, orother mobile operating systems. In this example, the third partyapplication 1442 invokes the API calls 1424 provided by the mobileoperating system, such as, operating system 1414 to facilitatefunctionality described herein.

In one example embodiment, one or more of the modules described in FIG.4 are at least partially implemented as applications 1420. Theapplications 1420 may utilize built-in operating system functions (e.g.,kernel 1428, services 1430, or drivers 1432), libraries (e.g., systemlibraries 1434, API libraries 1436, and other libraries 1438),frameworks/middleware 1418 to create user interfaces to interact withusers of the input correction system 150. Alternatively, oradditionally, in some systems interactions with a user may occur througha presentation layer, such as presentation layer 1444. In these systems,the application/module “logic” can be separated from the aspects of theapplication/module that interact with a user.

Some software architectures utilize virtual machines. In the example ofFIG. 14, this is illustrated by virtual machine 1448. A virtual machinecreates a software environment where applications/modules can execute asif they were executing on a hardware machine (such as the machine ofFIG. 15, for example). The virtual machine 1448 is hosted by a hostoperating system (operating system 1414 in FIG. 14) and typically,although not always, has a virtual machine monitor 1446, which managesthe operation of the virtual machine 1448 as well as the interface withthe host operating system (e.g., operating system 1414). A softwarearchitecture executes within the virtual machine 1448 such as anoperating system 1450, libraries 1452, frameworks/middleware 1454,applications 1456, or presentation layer 1458. These layers of softwarearchitecture executing within the virtual machine 1448 can be the sameas corresponding layers previously described or may be different.

Example Machine Architecture and Machine-Readable Medium

FIG. 15 is a block diagram illustrating components of a machine 1500,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 15 shows a diagrammatic representation of the machine1500 in the example form of a computer system, within which instructions1516 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1500 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1516 may cause the machine 1500 to execute theflow diagrams of FIGS. 8-13. Additionally, or alternatively, theinstructions 1516 may implement the input module 420, the selectionmodule 440, the filter module 460, and the correction module 480 of FIG.4, and so forth. The instructions 1516 transform the general,non-programmed machine into a particular machine programmed to carry outthe described and illustrated functions in the manner described.

In alternative embodiments, the machine 1500 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1500 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1500 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1516, sequentially or otherwise, that specify actions to betaken by machine 1500. Further, while only a single machine 1500 isillustrated, the term “machine” shall also be taken to include acollection of machines 1500 that individually or jointly execute theinstructions 1516 to perform any one or more of the methodologiesdiscussed herein.

The machine 1500 includes processors 1510, memory/storage 1530, and I/Ocomponents 1550, which may be configured to communicate with each othersuch as via a bus 1502. In an example embodiment, the processors 1510(e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) processor, a Complex Instruction Set Computing (CISC)processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an Application Specific Integrated Circuit (ASIC), aRadio-Frequency Integrated Circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, processor 1512and processor 1514 that may execute instructions 1516. The term“processor” is intended to include multi-core processor that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.15 shows multiple processors 1510, the machine 1500 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core process), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory/storage 1530 may include a memory 1532, such as a mainmemory, or other memory storage, and a storage unit 1536, bothaccessible to the processors 1510 such as via the bus 1502. The storageunit 1536 and memory 1532 store the instructions 1516 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1516 may also reside, completely or partially, within thememory 1532, within the storage unit 1536, within at least one of theprocessors 1510 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1500. Accordingly, the memory 1532, the storage unit 1536, and thememory of processors 1510 are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot be limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)), or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store instructions 1516. The term“machine-readable medium” shall also be taken to include any medium, orcombination of multiple media, that is capable of storing instructions(e.g., instructions 1516) for execution by a machine (e.g., machine1500), such that the instructions, when executed by one or moreprocessors of the machine 1500 (e.g., processors 1510), cause themachine 1500 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

The I/O components 1550 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1550 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1550 may include many other components that are not shown in FIG. 15.The I/O components 1550 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1550 mayinclude output components 1552 and input components 1554. The outputcomponents 1552 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1554 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1550 includesbiometric components 1556, motion components 1558, environmentalcomponents 1560, or position components 1562 among a wide array of othercomponents. For example, the biometric components 1556 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1558 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1560 include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1562 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1550 may include communication components 1564operable to couple the machine 1500 to a network 1580 or devices 1570via coupling 1582 and coupling 1572, respectively. For example, thecommunication components 1564 may include a network interface componentor other suitable device to interface with the network 1580. In furtherexamples, communication components 1564 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1570 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 1564 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1564 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1564, such as location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

In one example embodiment, the input module 420 may receive the userstrings from the network 104 using a communication device 1570. Theinput module 420 may also store received user string via the storageunit 1536. In another embodiment, the input module 420 receives the userstrings via an alpha-numeric input component 1554.

Transmission Medium

In various example embodiments, one or more portions of the network 1580may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1580 or a portion of the network 1580may include a wireless or cellular network and the coupling 1582 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or other type of cellular orwireless coupling. In this example, the coupling 1582 may implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard setting organizations, other long rangeprotocols, or other data transfer technology.

The instructions 1516 may be transmitted or received over the network1580 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1564) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1516 may be transmitted or received using a transmission medium via thecoupling 1572 (e.g., a peer-to-peer coupling) to devices 1570. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying instructions 1516 forexecution by the machine 1500, and includes digital or analogcommunications signals or other intangible medium to facilitatecommunication of such software.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: receiving a plurality ofuser strings in a session for a user; selecting one or more string pairsfrom the plurality of user strings, each selected string pair comprisinga first string and a second string, the second string resulting inincreased response from the user as compared with the first string, thefirst string and the second string selected based on a characteroperator difference between the first string and the second string beingbelow a threshold number; filtering, by a hardware processor, the one ormore string pairs to generate a filtered set of strings pairs, thefiltered set of string pairs comprising one or more string pairs wherethe second string of the respective string pairs is a correction of thefirst string in the respective string pair; and correcting user input ina different session by replacing a portion of the user input thatmatches the first string in one of the filtered string pairs with thefiltered string pair.
 2. The method of claim 1, wherein the first stringand the second string in each selected string pair is selected usingbigram character analysis.
 3. The method of claim 1, wherein thefiltering comprises removing a string pair from the one or more stringpairs in response to a language model use value for the first string inthe string pair being above a threshold value.
 4. The method of claim 1,wherein the filtering comprises removing a string pair from the one ormore string pairs in response to a language model use ratio being abovea threshold value, the language model use ratio being a ratio of alanguage model use value of the first string to a language model usevalue of the second string in the string pair to be removed.
 5. Themethod of claim 1, wherein the filtering comprises excluding a selectedstring pair in the filtered set of string pairs based on the string pairnot representing a correction.
 6. The method of claim 1, wherein thefiltering comprises excluding a selected string pair in the filtered setof string pairs based on the second string in the selected string pairbeing a refinement of the first string in the selected string pair. 7.The method of claim 1, wherein the filtering comprises excluding aselected string pair in the filtered set of string pairs based on eitherthe first string of the selected string pair or the second string in theselected string pair being in a predetermined language.
 8. Acomputer-implemented method comprising: receiving user strings from aplurality of users in their respective user sessions; selecting one ormore string pairs from the received user strings based on each of theone or more string pairs being received in more than one of the usersessions, the string pairs including a first string that resulted in nouser events in the respective user session and a second string thatresulted in a user event in the respective user session, a characteroperator difference between the first string and the second string beingbelow a threshold number; filtering, by a hardware processor, the one ormore string pairs to generate a filtered set of string pairs, thefiltered set of string pairs comprising string pairs received from athreshold number of the user sessions; training a statistical machinelanguage framework based on the filtered set of string pairs; andcorrecting user input in a different user session using the statisticalmachine language framework.
 9. The computer-implemented method of claim8, wherein the user input in the different user session includes asearch query, the method further comprising returning search resultsbased on the second string.
 10. The computer-implemented method of claim8, wherein the filtering the one or more string pairs is based on one ormore properties of one of the plurality of users.
 11. Thecomputer-implemented method of claim 8, wherein the character operatordifference is based on bigram character analysis.
 12. Thecomputer-implemented method of claim 8, wherein the filtering comprisesexcluding a string pair in the filtered set of string pairs based oneither the first string in the string pair or the second string in thestring pair being in a pre-determined language.
 13. Thecomputer-implemented method of claim 8, wherein the filtering comprisesexcluding a string pair in the filtered set of string pairs based on alanguage model use value for the first string in the string pair beingbelow a threshold use value.
 14. The computer-implemented method ofclaim 8, wherein the filtering comprises excluding a string pair in thefiltered set of string pairs based on a language model use ratio beingbelow a threshold value, the language model use ratio being a ratio of alanguage model use value of the first string of the string pair to alanguage model use value of the second string of the string pair. 15.The computer-implemented method of claim 8, further comprising flaggingthe string pair based on one or more characteristics of one of theplurality of users, the characteristics of the user included in a userprofile.
 16. A computer-implemented method comprising: receiving aplurality of user strings; selecting one or more string pairs from theplurality of user strings, each selected string pair comprising a firststring and a second string, the second string resulting in increasedresponse from the user as compared with the first string, the firststring and the second string selected based on a character operatordifference between the first string and the second string being below athreshold number; and correcting user input by replacing a portion ofthe user input that matches the first string in one of the string pairswith the second string in the string pair.
 17. The method of claim 16,wherein two or more string pairs match the portion of the user input,the correcting using the matching string pair resulting in a highernumber of responses.
 18. The method of claim 16, wherein two or morestring pairs match the portion of the user input, the string pairsincluding a category, the correcting using the string pair that matchesa category of a provider of the user input.
 19. The method of claim 18,wherein matching the category comprises determining a matchingprobability for each category in a set of categories, and selecting thecategory with the highest matching probability, the matching probabilitybased on machine learning.
 20. The method of claim 16, wherein thecharacter operator difference is based on Damereau-Levenshtein distance.